Protection Against Sea Level Rise and Storm Surge Prevents Severe Eco-nomic Losses: A Case Study in Copen-hagen

Climate change impacts in coastal cities are expected to represent a major challenge this century, with millions of exposed people and thousands of billions of USD of exposed assets at the global scale. As a low-lying city with a significant number of people and amount of property lying close to the water level, Copenhagen is potentially vulnerable to the effects of sea level rise. In their study, Hallegatte et al (2011) illustrate a methodology to estimate economic impacts of climate change at a city scale, taking the example of sea level rise and storm surge risks in Copenhagen. The authors’ approach is a simplified catastrophe risk assessment<!–[if supportFields]> XE “risk assessment” <![endif]–><!–[if supportFields]><![endif]–>, to calculate the direct costs of storm surges under scenarios of sea level rise, coupled to an economic input–output model. The output is a risk assessment of the direct and indirect economic impacts of storm surge under climate change including production and job losses. For Copenhagen, it is found that in absence of adaptation, sea level rise would significantly increase flood risks. Results call for the introduction of adaptation in long-term urban<!–[if supportFields]> XE “urban” <![endif]–><!–[if supportFields]><![endif]–> planning, as one part of a comprehensive strategy to manage the implications of climate change in the city. —Michelle Schulte
Hallegatte, S., Ranger, N<!–[if supportFields]>XE “nitrogen, N”<![endif]–><!–[if supportFields]><![endif]–><!–[if supportFields]> XE “nitrogen” <![endif]–><!–[if supportFields]><![endif]–>., Mestre, O., Dumas, P, Morlot, J.C., Herweijer, C., Wood, R.M. 2011. Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen. Climate Change 104, 113–137.

Due to local factors such as uplift and changes in ocean circulation, the water level in Copenhagen has risen at a rate of 4 cm a year while globally, the sea level has risen17 cm over the century. In terms of regional changes, the IPCC<!–[if supportFields]> XE “Intergovernmental Panel on Climate Change (IPCC)”<![endif]–><!–[if supportFields]><![endif]–> found that sea level rise could be greater than the global average around northern Europe<!–[if supportFields]> XE “Europe” <![endif]–><!–[if supportFields]><![endif]–>, reaching up to 38–79 cm around Denmark. Because of this large uncertainty of sea level rise, several possible amplitudes of SLR<!–[if supportFields]>XE “sea-level rise (SLR)”<![endif]–><!–[if supportFields]><![endif]–> are considered, from 0 to 125 cm, and results are presented for all the cases. The authors analyzed the impact of climate change through a series of steps: (1) a statistical analysis of past storm surges in Copenhagen; (2) a geographical-information analysis of the population and asset exposure in the city, for various sea levels and storm surge characteristics; (3) an assessment of direct economic losses in case of storm surge; (4) an assessment of the corresponding indirect losses—in the form of production and job losses, reconstruction duration etc.—using an adaptive regional input–output model (ARIO); and (5) a risk analysis of the effectiveness of coastal flood protections, including risk changes due to climate change and sea level rise.
Hallegatte et al. found that in the absence of protection, potential losses would increase over time. The authors analyzed the total, direct losses of public and private (insured) land, defining direct losses as the repair and replacement cost of damaged buildings and equipment due to flooding. With 25 cm of mean sea level rise, total losses caused by a future 100-year event would rise from €3 billion to €4 billion. When this storm was coupled with a 50 cm sea level (or a total SLR<!–[if supportFields]>XE “sea-level rise (SLR)”<![endif]–><!–[if supportFields]><![endif]–> of 2 m), the damages increased by 55% to roughly €5 billion, and to €8 billion with 100 cm sea level. Thus, without protection, sea level rise increases the risk of flooding significantly.
Direct losses caused by an event are usually significantly lower than the exposure to this event. There is a complex link between exposure to high sea level and the destruction and losses caused by such episodes. The total cost of flooding in Copenhagen is equal to the sum of direct and indirect costs. The indirect cost is the reduction in production of goods and services across the economy due to the disaster. The authors analyzed the impact of a 2 m increase in sea level above present-day values on 8 sectors of value added (VA). In the early period following a storm surge, the losses and gains in VA are estimated to roughly balance each other due to reconstruction efforts. However, a total of 7,500 jobs are lost in the 3 months after the disaster and 500 jobs are lost 1 year after the shock. The sectors that are most impacted include wholesale and retail trade, finance and business activities, and transportation. However, the authors argue that adaptation measures have to focus on direct loss reduction (using dikes or reinforced buildings) as direct losses are much more vast than indirect losses.
Copenhagen is currently easy to protect against storm surges, but needs additional protection against rising sea levels. While annual mean losses can reach several billions of Euros with protection of less than 1 m, they decrease very rapidly with protection height. Economic losses decrease to less than €100,000 per year for 180 cm of protection, and null for protection higher than 202 cm. Therefore, the authors estimated that construction cost of coastal flood protection of 2 or 3 m to be a few hundred million Euros for the city. Hallegatte et al. assert that despite 202 cm of protection, a 25 cm SLR<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> causes €1 million a year in damages while a 100 cm SLR causes €4.2 billion per year. On the other hand, with 300 cm protection, damages only occur if SLR are >1 m. In addition, the timescale of the increases in losses cannot be determined, because of uncertainty in future SLR. In the most optimistic scenarios, sea level rise should not exceed 25 cm by 2100 while the most pessimistic studies show that SLR could exceed 1 m by 2100.
Copenhagen is very well protected against storm surges and coastal flooding due to its high standards of defense. First, in the city center and the harbor, quays are at more than 2 m above current sea level. Considering that the authors estimate the maximum possible storm surge at 2 m, this protection level suggests that this part of the city is not at risk. In locations that are at-risk, protection is present in the form of dikes. In addition, even a large SLR<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> could be managed by the current protection system. Only a few areas could be affected by storm surges with the current sea level and with higher sea levels. In these areas, protection will have to be upgraded to prevent coastal flood risk from increasing rapidly across the ranges of SLR considered in this study.

The Optimum Economic Response to Substantial Sea-Level Rise is Wide-spread Protection of Developed Coastal Areas

Rapid sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> (> 1 m/century) raises concern as it is believed that this would lead to large losses and a widespread forced coastal retreat. Anthoff et al. (2010) aimed to estimate economic damages caused by substantial sea-level rise and clarify the extent societies can protect themselves from rising sea levels. While the costs of sea-level rise increase with greater rise due to growing damage and protection costs, the integrated assessmentmodel (FUND) suggests that an optimum response in a benefit-cost sense remains widespread protection of developed coastal areas. The benefits of protection increase significantly with time due to the economic growth assumed in the SRES socio-economic scenarios. In terms of the four components of costs considered in FUND, protection dominates, with substantial costs from wetland loss. The regional distribution of costs shows that a few regions experience most of the costs, especially East and South Asia, North America, and Europe<!–[if supportFields]> XE “Europe” <![endif]–><!–[if supportFields]><![endif]–>. The analysis and computer model contain some limitations so that protection may not be as widespread as suggested in the FUND results. However, the FUND analysis shows that protection is more likely and rational than is widely assumed, even with a large rise in sea level. —Michelle Schulte
Anthoff, D., Nicholls, R., Tol, R., 2010. The economic impact of substantial sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–>.
Mitigation and Adaptation Strategies for Global Change 15, 321–355.

The authors utilize the Coastal Module of FUND 2.8n to calculate damages caused by various scenarios of sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> over the next century. The model is driven by five distinct socio-economic scenarios (four well-known SRES scenarios and a control scenario) of population and GDP (gross domestic product) growth on a per country scale. Sea-level rise is treated as a linear interpolation with three distinct scenarios of 0.5 m, 1.0 m, and 2.0 m above 2005 sea levels in 2100. Rising sea levels are assumed to have four damage cost components: the value of dryland cost, the value of wetland cost, the cost of protection (with dykes) against rising sea levels and the costs of displaced people that are forced to leave their original place of settlement due to dryland loss. FUND determines the peak amount of protection based on the socio-economic situation, the expected damage of sea-level rise if no protection existed, and the necessary protection costs.
The number of people displaced is a linear function of dryland loss and the average population density in a country. The area of dryland loss is assumed to be a linear function of sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> and protection level up to 2 m of sea-level rise. Wetland value, on the other hand, is assumed to be proportional to per capita income with a correction for wetland scarcity and a cap. Conceptually, the value of wetlands at first rises very rapidly with income, but it increases much more slowly if incomes and wetland values are very high. The average annual protection costs are assumed to be a bilinear function of the rate of sea-level rise as well as the proportion of the coast that is protected. The level of protection is based on a cost-benefit analysis that compares the costs of protection (the actual construction of the protection and the value of the wetland lost due to the protection) with the benefits, i.e. the avoided dryland loss. The authors also continue on to create functions to control for the value of the wetlands lost due to protection and the value of the dryland lost if no protection takes place. Lastly, Anthoff et al. used a standard Ramsey discount rate to compute the net present value total damage costs for the period of 2005–2100.
First, the authors analyzed the global damage costs by socio-economic and sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> scenario. Anthoff et al. found that while the choice of socio-economic scenario has an influence on the global damage costs from sea-level rise, the damage costs vary more depending on the sea-level rise scenario. The damage costs for a 1 m rise are between 4.8 and 5.2 times as high as the damage costs for the 0.5 m sea-level rise, depending on the scenario. The increase in costs from 1 m to 2 m is only 2.0 times the damage cost of the 1 m sea-level rise scenario. The overall difference between the SRES scenarios is small.
Secondly, the authors broke apart the damage costs by socio-economic and sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> scenarios. At a 0.5 m sea-level rise, protection costs followed by wetland loss are the most important damage cost component for each socio-economic situation. Protection costs are affected by dryland loss and migration costs more so than the socio-economic scenario. When the sea-level rise is then increased to 1.0 m, the wetland costs are the damage components that react roughly linearly. Protection costs increase between 4.2 to 6.6 times compared to the lower sea-level rise while dryland loss and migration costs increase by an order of magnitude. While the step from 0.5 m to 1 m sea-level rise changed the distribution of costs between the four components significantly, the step to the 2 m scenario has no such surprises. All costs roughly double compared to the 1 m scenario. This is not surprising since the model does not have a change in cost assumptions in this step.
Thirdly, sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> damages are not evenly distributed over the world. The regional distribution of the costs shows that a few regions experience most of the costs, especially South Asia, South America, North America, Europe<!–[if supportFields]>XE “Europe” <![endif]–><!–[if supportFields]><![endif]–>, East Asia, and Central America. Next, under a scenario of no protection, the costs of sea-level rise increase greatly due to the increase in land loss and population displacement; this scenario shows the significant benefits of the protection response in reducing the overall costs of sea-level rise. Furthermore, dikes along the coastline can significantly lower total damages, but only when economic growth enables this sometimes costly investment in protection to occur. Hence protection and economic growth are coupled. In densely populated and rich countries, dike building has a high return in that a small expense prevents substantial damage. If people are dispersed and poor, the pay-off to coastal protection is much smaller. For the 0.5 m sea-level rise, total damages are between 3.4 and 3.7 times higher when no protection is built for that scenario. For 1 m and 2 m sea-level rise the damages in the no-protection scenario are only around 1.4 times as high compared to a protection scenario. This change is due to an increase in magnitude of protection costs as illustrated previously.

Protection may not become as widespread as suggested in this analysis, especially for the 2 m sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> scenario. The aggregated scale of analysis in FUNDmay overestimate the extent of likely protection in certain countries. Also, the SRES socio-economic scenarios are quite optimistic about future economic growth. Lower growth will reduce the capactiy to protect. The benefit-cost approach implies a proactive approach to protection, while historical experience shows that protection is in reaction to actual or near coastal disaster. Lastly, the economics of who pays and who benefits in coastal protection influence society’s choices and ability to protect the coast. Despite all of this, the authors assert that the FUND analysis shows that protection is more likely and rational than is widely assumed, even with a large rise in sea level.

The Intertidal Communities on Dissipa-tive Beaches at Risk for Sea Level Rise—The Relationship of Beach Mor-phodynamics and Species Range.

Sea-level rise is likely to cause significant physical changes to beaches in the higher latitudes, resulting in steeper beaches with larger particle sizes. These physical changes have implications for beach invertebrate communities, which are determined largely by sediment particle size, and hence for ecosystem function. Previous studies have explored the relationships between invertebrate communities and environmental variables such as particle size, beach slope, and exposure to wave action. Yamanaka et al. (2010) quantified the abundance of meiofauna and macrofauna across a range of beaches in the UK. The authors confirmed the predominant role of beach physical factors in determining infaunal species composition on the less wave-dominated beaches typically found over much of the European coastline. The more dissipative beaches, or the flat beaches with finer particles and gentler slopes, had a higher density of organisms, but a smaller range of species richness. If predictions that accelerated sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> will move beaches towards a more reflective morphodynamic state are correct, this could lead to potential adverse consequences for ecosystem functioning through the declining abundance of benthic organisms between 0.3 and 1mm in size. —Michelle Schulte
Yamanaka, T., Raffaelli, D., White, P.C.L., 2010. Physical determinants of intertidal communities on dissipative beaches: Implications of sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–>. Estuarine, Coastal and Shelf Science 88, 267–278.

The authors utilized various indices of beach morphodynamic state to quantify the physical characteristics of beaches in three different estuarine locations on the east coast of the UK that experience different tidal ranges, slopes, and range of particle size. The three contrasting field sites in the UK are the Humber estuary, the Ythan estuary, and the Firth of Forth. Five or six sampling sites were selected within each locality, restricted to a short area of the outer estuary or coastal site in order to minimize any potentially confounding effects of salinity<!–[if supportFields]> XE “salinity” <![endif]–><!–[if supportFields]><![endif]–>. At each station, a cylindrical core was pushed into the sediment to the depth of 10 cm on a randomly chosen surface to sample macrofauna, meiofauna, and sediment. Macrofauna were separated from sediment using a 500 µm mesh, preserved in 70% ethanol<!–[if supportFields]> XE “ethanol” <![endif]–><!–[if supportFields]><![endif]–>, identified to species level, and counted using a microscope. Meiofauna were separated from sediment using a 64 µm mesh, preserved in ethanol, and stained with Rose Bengal, identified to the lowest possible taxon, and counted. Particle size was determined by dry sieving through a tower of mesh sieves. The slope at each sampling station was calculated by measuring the height and distance of the sample site. The exposure at each beach site was calculated using the index derived from wind velocity, direction, duration, and the effective fetch.
Yamanaka et al. created new indices to determine the morphodynamic state of the beach and the wave energy. A combination of non-metric Multi Dimensional Scaling (NMDS), and an eigenvector-based approach, DCA, was used, in conjunction with cluster analysis to explore the main trends and patterns in the data in terms of physical and biological variables of the sites. In addition, stepwise multiple linear regression was used to explore the relationships of abundance and number of species with morphodynamic state. One-way analyses of variance (ANOVA<!–[if supportFields]> XE “ANOVA” <![endif]–><!–[if supportFields]><![endif]–>) were used to test the importance of each independent variable, and also to test the difference of physical variables between the three areas.
The authors explored the relationships between beach fauna and morphodynamic variables, to test whether more dissipative beaches support a high abundance of macrofauna and meiofauna as well as higher macrofaunal species richness. The authors ask how these relationships may inform our understanding of the impacts of sea-level rise<!–[if supportFields]>XE “sea-level rise (SLR)”<![endif]–><!–[if supportFields]><![endif]–> on benthic community structure and function. They compared the differences in the physical characteristics of each of the beaches. Median particle size was not significantly different between estuaries, but beach slope and wave exposure differed significantly. The Humber had a much higher range of exposures and a shallower beach slope than the Ythan and the Forth.
The fauna within these three sites differed in their composition and abundance. There was more overlap in species composition between the Humber and the Ythan, despite an order of magnitude difference in abundance. For each scenario, the more dissipative beaches contained higher abundances of all fauna. So that dissipative beaches with finer particles and shallow slopes generally support a higher abundance of macrofauna.
However, for species richness, Yamanaka et al. found that less dissipative beaches generally support higher macrofaunal species richness. Both the Ythan and the Humber had lower species richness compared to the Forth, but differed markedly in the numbers of individuals recorded. The Forth had an intermediate number of macrofauna individuals but the most taxa represented. In addition, the authors found that the length of exposure to the sun and the beach slope affect the abundance of small, benthic organisms. There were no clear relationships between diversity indices and beach physical variables.
Yamanaka et al. confirmed the predominant role of beach physical factors in determining infaunal species composition on the less wave-dominated beaches typically found over much of the European coastline. All of the species recorded can be described as deposit feeders, filter feeders, or predators. Past studies illustrate that large polychaetes are disproportionately important for ecosystem processes such as nutrient cycling. Thus, functional diversity and compositional effects rather than species richness, may play an important role in driving ecosystem processes. A greater diversity of large species including polychaete species was found at more sheltered sites on the Ythan and the Forth. If sea-level rise<!–[if supportFields]>XE “sea-level rise (SLR)”<![endif]–><!–[if supportFields]><![endif]–> pushes beaches towards steeper slopes and coarser particles, as indicated in the study by Yamanaka et al., then the abundance of these larger species is likely to decline, with consequent reduction in ecosystem functioning.
In summary, the authors illustrate the validity of the trend that more dissipative beaches have a higher abundance of macrofauna and meiofauna compared to reflective beaches when analyzing less wave-dominated beaches. In addition, the authors suggest that sea-level rise<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> could have a significant impact on ecosystem functioning in northern temperate beaches, through the effects of changing particle size and wave exposure on benthic species richness and abundance, especially the larger-bodied polychaetes. 

First Global Assessment of Terre-strial Biodiversity Consequences of a 1–6 m Sea-Level Rise

Considerable attention has focused on the effects of global climate change on biodiversity, but few analyses and no broad assessments have evaluated effects of sea-level rise on biodiversity. Menon et al. calculated the total area lost for all terrestrial ecoregions using new maps of marine intrusion under scenarios of 1 and 6 m sea-level rise. Areal losses for particular ecoregions ranged from nil to complete. Marine intrusion is a global phenomenon, mostly affecting Southeast Asia and nearby islands, eastern North America, northeastern South America, and western Alaska. Assuming that fauna respond to reduced ecoregions in a predictable manner, the authors estimated likely numbers of extinctions caused by sea-level rise. They found that northeastern South America is most susceptible to marine-intrusion-caused extinctions, although anticipated extinctions in smaller numbers will be scattered worldwide. This assessment is the first global analysis of sea-level rise impacts on terrestrial biodiversity, complementing recent estimates of losses owing to changing climatic conditions. —Michelle Schulte
Menon, S., Soberón, J., Xingong, L., Peterson A.T., 2010. Preliminary global assessment of terrestrial biodiversity consequences of sea-level rise mediated by climate change. Biodiversity Conservation 19, 1599–1609.

Past studies indicated that the rate of future melting of polar ice sheets and related sea-level rise could be faster than widely thought, resulting in a sea-level rise of 4–6 m by 2100. Menon et al. used geographic information systems (GIS) to delineate potential inundation areas resulting from projected sea-level rise of 1 and 6 m. In this analysis, cells below a projected sea-level rise that connect to the ocean and are not presently inland water are designated as inundation cells. The authors used the Terrestrial Ecoregions GIS Database and the Terrestrial Ecoregions Base Global Dataset as a source of geospatial data showing the global extent of ecoregions, as well as providing data on numbers of endemic species in each ecoregion. Menon et al. used values for strict endemic species and near-endemic species across all 827 terrestrial ecoregions in this analysis. They then converted the vector-format terrestrial ecoregions coverage into a grid, so as to estimate the area lost from marine intrusion by overlaying it with the 1 and 6 m inundation scenarios grids and performing raster map algebra.
A decrease in the area of an ecoregion can be used to estimate biodiversity losses under certain sets of assumptions. Past studies have employed the relationship between the numbers of species present and area under consideration (species–area relationship, or SAR) to calculate future extinctions. The SAR is a steady-state relationship between number of species (S) and area (A) of the form S = cAz, where cand z are constants estimated empirically. If the present number of species Snow is existing in an area Anow, which is reduced to Afuture, and if c and z remain constant, then the number of species will eventually decrease to a new steady state Sfuture = Snow (Afuture/Anow)z. The authors estimated z in two different ways: as the overall SAR across all ecoregions globally, and SARs for 3 latitudinal bands (polar, temperate, tropical). The authors calculated Sfuture for each ecosystem under the general z and the latitude-specific z, and estimated confidence intervals for each Sfuture.
Globally, 0.7% of global land was inundated and therefore lost under 1 m of sea-level rise, and 1.5% of global land area under 6 m of sea-level rise. Proportional losses in ecoregions ranged from 0 to 100%. The most affected ecoregions were Southeast Asia and associated islands, northeastern South America, eastern North America, and western Alaska. Even under a 1 m sea-level rise scenario, 21 ecoregions are expected to lose >50% of their land area, which include 8 mangrove-dominated ecoregions, lowland forest and scrub on 8 islands, and 5 low-lying continental areas. Thus, sea-level rise manifested as marine intrusions is expected to greatly affect terrestrial ecoregions.
For the global SAR fitting, z was estimated at 0.124 ± 0.015 s.e., although the overall fit was not particularly tight. Out of a total of 18,628 endemic or near-endemic species in single ecoregions, this single SAR yielded a calculated loss of 117 ± 27 species for the 1 m sea-level rise scenario, and 221 ± 51 species for the 6 m scenario. Splitting SAR regressions into polar, temperate, and tropical subsets, important regional differences were observed. The slope of the SAR (z) was highest in tropical regions, and lowest in polar regions. Also, these SAR differences translated into different rates of estimated species loss under the 1 and 6 m scenarios: 0 of 35 polar species under both scenarios; 10 and 30 out of 3,117 species in temperate regions; and 170 and 307 out of 15,476 species in tropical regions. Overall, with region-specific z estimates, global species losses sum to 181 ± 23 species under the 1 m scenario and 337 ± 44 species under the 6 m scenario, out of 18,628 current species.
Past studies have criticized on a number of grounds the use of the linear relationship of species and area to estimate future extinctions. Menon et al., however, controlled for the possible errors given the data limitations in resolution of area and in the taxon, range, and fragmentation of species. Certainly, both the marine-intrusion and the biodiversity distribution summaries could be improved significantly. For the marine-intrusion scenario, improvements are needed in the horizontal (from 1 km to 10 m resolution) and vertical (<1 m) resolutions. Also, moving from crude ecoregion-based summaries to actual species-specific distributional information would improve the estimates of the biodiversity distribution. Finally, because some species, such as keystone species, may play more critical roles in maintaining communities than others, categorizing the individual species as to their relative ‘importance’ in community structuring will clarify the magnitude of secondary effects.
Overall, the authors present a valid preliminary assessment of likely biodiversity consequences of sea-level rise and marine intrusion caused by climate change. The most realistic scenario of the two that were explored is a rise of 1 m by 2100, although the 6 m scenario is still very possible given the effects of glacial calving and ice-sheet loss. This analysis does not account for second-order effects on biodiversity caused by humans affected by rising sea levels, such as migrations and land use shifts, which may cause yet more negative effects on natural systems. 

Economic and Ecological Effects of Sea Level Rise on Coastal Wetlands: A Case Study from Galveston Island, Texas

Coastal salt marsh wetland plants are expected to migrate upslope with the rise in sea level, but human development is expected to limit the potential migration. Feagin et al. (2010) explored the ecological and economic effects of projected Intergovernmental Panel on Climate Change<!–[if supportFields]> XE “Intergovernmental Panel on Climate Change (IPCC)” <![endif]–><!–[if supportFields]><![endif]–> (IPCC) 2007 report sea level changes at the plant community scale using the highest horizontal and vertical resolution data available. Their findings demonstrate that salt marshes do not always lose land with increasing rates of sea level rise. The lower bound of the IPCC 2007 potential rise actually increased the total marsh area, resulting in a net gain in ecosystem service values on public property. The upper rise scenario resulted in both public and private economic losses for this same area. Overall, Feagin et al. highlight the trade-offs between public and privately held value under the various IPCC 2007 climate change scenarios. As wetlands migrate inland into urbanized regions, their survival is likely to be dependent on the rate of return on property and housing investments.—Michelle Schulte
Feagin, R.A., Martinez, M.L. Mendoza-Gonzalez, G, Costanza, R., 2010. Salt marsh zonal migration and ecosystem service change in response to global sea level rise: A case study from an urban<!–[if supportFields]> XE “urban” <![endif]–><!–[if supportFields]><![endif]–> region. Ecology and Society 15, 14–32.

The authors chose Galveston Island, Texas, USA as the study site for projected sea level rise as the sea level has been well documented in the past. The coastal salt marshes at the study site exhibited the zonation patterns common to other Spartina alterniflora-dominated marshes in the U.S.. Five plant community zones have been previously defined as: open water, low marsh, salt flat, high marsh, and upland. The authors then created a map of the plant community zone based on elevation using the highest horizontal (1 m) and vertical (0.01 m) resolution Light Detection And Ranging (LIDAR) data available. Because the resolution of this model is quite fine, spatially and species-wise, Feagin et al. illustrate the effect of sea level rise within a discrete 6 x 6 km extent. Three IPCC<!–[if supportFields]> XE “Intergovernmental Panel on Climate Change (IPCC)” <![endif]–><!–[if supportFields]><![endif]–> scenarios were implemented in a time step fashion up to the year 2095 using a low rise (0.18 m increase in sea level), a mid rise (0.39 m), and a high maximum rise (0.59 m).
After running the model, the expected plant habitat loss/gain was calculated for all of the scenarios, both including and excluding potential barriers to plant migration. The authors’ goal was to best represent the different plant community zones in this salt marsh relative to one another, in terms of market and non-market based values. The ecosystem services being provided by each plant community at the study site were identified. Monetary values were associated with recreation, hunting and bird watching tourism values, carbon sequestration<!–[if supportFields]> XE “carbon sequestration” <![endif]–><!–[if supportFields]><![endif]–>, storm protection, fisheries support, and market-based property appraisal values. To estimate gains and losses, the authors calculated ecosystem service values considering the different areas covered by each ecosystem, given the modeled climate change.
The zonal migration of the plant community zones primarily depends on the relative sea level rise rate, the accretion rate as specific to zone and location, and the availability of land at a suitable base elevation. Also, the choice of whether to remove human-erected barriers greatly affects the availability of the land on which this migration could occur. There is no significant change, except at the high-marsh-to-upland interface, in the plant community zones for the low-rise scenario between 2005 and 2095. The rate of sea level rise equaled the rate of accretion. The anthropogenic barriers limited plant migration in the upland areas while protecting the developed land. In the mid-rise scenario, there was a net loss of Spartina alterniflora-dominated low marsh. There was, however, a net gain of salt flats and high marsh as these two plant communities found more locations at suitable elevations as they migrated upslope. Under the IPCC<!–[if supportFields]> XE “Intergovernmental Panel on Climate Change (IPCC)” <![endif]–><!–[if supportFields]><![endif]–> high scenario, the low marsh and salt flat zones surprisingly fared better than in the mid rise scenario because of the topographic relief. The slope appeared to be the primary factor in determining the plant community distribution in the study area.
In the different SLR<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> scenarios, it was predicted that the economic losses will generally outweigh the gains. The models indicated that there will only be economic gains in Spartina alterniflora-dominated low marshes during a low rise event. The uplands, with large property appraisal values, are likely to show large economic losses in all the projected scenarios. In addition, if property investments accumulate at a 3% rate, the net economic value will be greater when the barriers to plant migration are removed. But if this value increases to a rate of 6%, then the optimal solution is to leave the barriers in place. This divergence highlights the trade-offs between public and private value because low marshes and open water are on public property; they are navigable waters and sit below the mean tide line.
This study shows that a salt marsh does not always lose land with increasing rates of sea level rise. The response of each individual plant community zone is more nuanced. Direct human activities and intervention in the migration process are estimated to account for the large majority of the losses that are predicted to occur this century. Rising sea levels and inflating property values will likely interact to reduce the incentive to save wetlands. The results show that the financial incentive to secure private property with barriers will increase by several orders of magnitude, given the IPCC<!–[if supportFields]> XE “Intergovernmental Panel on Climate Change (IPCC)” <![endif]–><!–[if supportFields]><![endif]–> high SLR<!–[if supportFields]> XE “sea-level rise (SLR)” <![endif]–><!–[if supportFields]><![endif]–> scenario over the low-rise scenario. In conclusion, as wetlands migrate landward, their survival is also dependent on the rate of return on property and housing investments. Local conditions and human proclivities will radically differentiate the benefit and costs of sea level rise around the world.

Sea-Level Rise Expected to Cause Significant Habitat Loss on the Barrier Islands in New York, Thereby Reducing Breeding Habitat of Piping Plover

Habitat loss, a leading threat to wildlife, is expected to escalate under global climate change resulting in the extinction of many species. Climate change is likely to raise sea levels by 0.18 m to 2 m over the next century, threatening many low-lying coastal areas such as the mid-Atlantic shoreline. Seavey et al. (2010) assessed the threat of sea-level rise (SLR) on the federally threatened piping plover (Charadrius melodus) on the barrier islands of Suffolk County, New York. The authors determined the extent of habitat change over the next 100 years under several SLR predictions. The results illustrate that if plover habitat cannot migrate, SLR is likely to reduce breeding areas. However, if habitat is able to migrate upslope and inland, breeding areas could actually increase. Unfortunately, this potential habitat gain is stymied by human development, which was found to reduce migrating habitat by 5–12%. The migration of potential habitat area was inhibited mostly by the spatial configuration of developed areas rather than the intensity of development. If the relative amount of plover habitat increases, human-plover conflict will likely arise as well. Finally, a large hurricane could flood up to 95% of plover habitat, thereby highlighting the risk from the synergism between SLR and coastal storms. To assure the future of plover habitat on these barrier islands, the authors assert that management needs to promote natural overwash and habitat migration, while minimizing development adjacent to future breeding habitat.—Michelle Schulte
Seavey, J.R., Gilmer, B., McGarigal, K.M., 2010. Effect of sea-level rise on piping plover (Charadrius melodus) breeding habitat. Biological Conservation 144, 393–401.

To study the potential change in piping plover breeding habitat with rising sea levels, the authors analyzed the barrier island system of Suffolk County, which spans 93 km of barrier island and peninsula shoreline along the southern coast of Long Island, New York. Multiple inlets break this barrier system into four segments. The islands are approximately 6 km by 0.1 km for the smallest and 50 km by 2.6 km for the largest. These dimensions are not stable, as island profiles are shifting and dynamic. The elevation of these islands is almost entirely below 3.5 m. Human development within the system is highly variable. Seavey et al. modeled two possible responses of plover habitat to SLR: static and dynamic. In the static habitat response, it was assumed that SLR would occur at a rate that outpaces the migration of habitat and the islands themselves. In this model, the spatial distribution of habitat was fixed and the rising sea level simply submerged land and existing habitat, resulting in a new spatial configuration of remaining habitat. A static habitat response is expected if the rate of SLR outpaces the ability of flora and fauna to migrate upslope and/or if development blocks movement of the landform. The second response model allowed for a dynamic habitat response wherein habitat could shift upslope and inland, redistributing itself based on the underlying landform. This habitat response was based on a plovar breeding habitat map created previously.
Using a global positioning system, the authors delineated the inland habitat boundary based on the presence of dense vegetation, steeply eroded banks, or human-made structures along the entire barrier island coastline of Suffolk County. The ocean-side habitat boundary was delineated as the high water line. The final format of this habitat map was an ESRI raster grid with 5 m horizontal resolution. This grid served as the base map for the analysis of the static habitat response and the binary response variable in a logistic generalized linear model (GLM) used to predict plover breeding habitat under the dynamic habitat response. Four well-supported SLR scenarios were chosen to model habitat changes. Each scenario represented a 30-year average SLR prediction, centered on 2080. Three of the four SLR scenarios are based on Intergovernmental Panel on Climate Change (IPCC) and New York City Panel on Climate Change estimates. The scenarios are B1 (0.38 m rise). A1B (0.47 m rise), and A2 (0.5 m rise). The fourth SLR scenario was based on recently verified rates of ice sheet loss and it stipulated a SLR of 1.5 m, higher than IPCC predictions. The four SLR scenarios, plus no SLR, were applied to both the static and dynamic habitat response models.
In addition, development data including buildings, roads, jetties and groins were digitized to create a development intensity surface. The authors wanted to compare the influence of development on the dynamic habitat response models by systematically examining each SLR scenario under various levels of development intensity. Next, Seavey et al. examined the risk of storm-induced plover habitat flooding under the 1.5 m SLR. Three types of storms were used: 5-year storm surge average (1.65 m), category-two hurricanes (0–2.4 m), and category- three hurricanes (0–3.7 m). The amount of plover habitat flooded by each storm type was calculated by clipping the resulting 1.5 m dynamic SLR with development habitat map by each storm flood extent.
The response of the barrier island plover habitat to SLR (i.e., static versus dynamic response) can make a large difference in predictions of future habitat. Habitat migration allowed for an increase in plover habitat with SLR in Suffolk County, New York. This increase resulted from the specific topography of these particular islands, which has more land area at higher elevations and inland compared to the current position of plover habitat. However, the ability of plover habitat to migrate across this particular landscape is uncertain and complex. Without considering the influence of development, the pattern of habitat change under increasing SLR differed greatly between the static and dynamic habitat response models. Potential piping plover breeding habitat area was reduced by as much as 41% under the static response model. In contrast, in the dynamic model habitat area grew by as much as 15%. This increase in relative amount of habitat reflected the steady loss of the barrier island system in this model. Under the dynamic response, the study area was also lost due to flooding; however, the habitat redistributed itself across the landscape in greater proportion. As the SLR estimate increased, the amount of plover habitat went from 32% to 65% of the total barrier island system. Furthermore, the authors assert that the future of plover habitat with rising sea levels will be dictated, in large part, by how coastal development is zoned and managed.
Regardless of the migration response, SLR in combination with the predicted increase in storminess due to climate change is likely to increase nest failure. Storm surge flooding impacted a large proportion of the projected habitat under the 1.5 m SLR with development scenario. The 5-year storm and category-two hurricane surge flooded about 75% of potential nesting habitat; whereas a category-three hurricane surge flooded over 95% of the area. Among the piping plover nests found in the study area during the 2003–2005 breeding seasons, 74% of nests would have been flooded by a 5-year storm, 73% by a category-two hurricane, and 97% by a category-three hurricane. The large impact from all storm types stemmed from the relatively low elevation of the barrier island system in Suffolk County. While it is uncertain what the loss of one breeding season would mean to the overall plover population, the increased frequency of large storms predicted to accompany global climate change may make nest flooding more frequent and likely to increase population risk.
Their results raise concern over the potential for SLR to increase human-plover conflict. Both habitat response models predict an increase in the proportion of the island areas in potential plover habitat over the next 100 years. If the relative amount of plover habitat increases, conflict is likely to arise especially as the human population in the region grows. Moreover, interspecies competition for nesting space and other resources may increase as plovers, American oystercatchers (Haematopus palliatus), least terns (Sternula antillarum), common terns (Sterna hirundo), and other coastal species are crowded together.

Habitat loss resulting from SLR, especially along low-lying, developed coastlines, is likely to increase piping plover extinction risk. To avoid the potential loss of plover habitat, management actions must be based on the assumption that coasts are dynamic, highly variable, and will shift with rising sea levels. Today’s plover nesting habitat is unlikely to be suitable, or even exist, in the near future. Management will need to be adaptive and focus on actions that restrict and even reduce development so that ecological processes, such as overwash and habitat migration, are preserved.

The Optimum Economic Response to Substantial Sea-Level Rise is Widespread Protection of Developed Coastal Areas

Rapid sea-level rise (> 1 m/century) raises concern as it is believed that this would lead to large losses and a widespread forced coastal retreat. Anthoff et al. (2010) aimed to estimate economic damages caused by substantial sea-level rise and clarify the extent societies can protect themselves from rising sea levels. While the costs of sea-level rise increase with greater rise due to growing damage and protection costs, the integrated assessment model (FUND) suggests that an optimum response in a benefit-cost sense remains widespread protection of developed coastal areas. The benefits of protection increase significantly with time due to the economic growth assumed in the SRES socio-economic scenarios. In terms of the four components of costs considered in FUND, protection dominates, with substantial costs from wetland loss. The regional distribution of costs shows that a few regions experience most of the costs, especially East and South Asia, North America, and Europe. The analysis and computer model contain some limitations so that protection may not be as widespread as suggested in the FUND results. However, the FUND analysis shows that protection is more likely and rational than is widely assumed, even with a large rise in sea level.¾Michelle Schulte
Anthoff, D., Nicholls, R., Tol, R., 2010. The economic impact of substantial sea-level rise. Mitigation and Adaptation Strategies for Global Change 15, 321–355.

The authors utilize the Coastal Module of FUND 2.8n to calculate damages caused by various scenarios of sea-level rise over the next century. The model is driven by five distinct socio-economic scenarios (four well-known SRES scenarios and a control scenario) of population and GDP (gross domestic product) growth on a per country scale. Sea-level rise is treated as a linear interpolation with three distinct scenarios of 0.5 m, 1.0 m, and 2.0 m above 2005 sea levels in 2100. Rising sea levels are assumed to have four damage cost components: the value of dryland cost, the value of wetland cost, the cost of protection (with dykes) against rising sea levels and the costs of displaced people that are forced to leave their original place of settlement due to dryland loss. FUND determines the peak amount of protection based on the socio-economic situation, the expected damage of sea-level rise if no protection existed, and the necessary protection costs.
The number of people displaced is a linear function of dryland loss and the average population density in a country. The area of dryland loss is assumed to be a linear function of sea-level rise and protection level up to 2 m of sea-level rise. Wetland value, on the other hand, is assumed to be proportional to per capita income with a correction for wetland scarcity and a cap. Conceptually, the value of wetlands at first rises very rapidly with income, but it increases much more slowly if incomes and wetland values are very high. The average annual protection costs are assumed to be a bilinear function of the rate of sea-level rise as well as the proportion of the coast that is protected. The level of protection is based on a cost-benefit analysis that compares the costs of protection (the actual construction of the protection and the value of the wetland lost due to the protection) with the benefits, i.e. the avoided dryland loss. The authors also continue on to create functions to control for the value of the wetlands lost due to protection and the value of the dryland lost if no protection takes place. Lastly, Anthoff et al. used a standard Ramsey discount rate to compute the net present value total damage costs for the period of 2005–2100.
First, the authors analyzed the global damage costs by socio-economic and sea-level rise scenario. Anthoff et al. found that while the choice of socio-economic scenario has an influence on the global damage costs from sea-level rise, the damage costs vary more depending on the sea-level rise scenario. The damage costs for a 1 m rise are between 4.8 and 5.2 times as high as the damage costs for the 0.5 m sea-level rise, depending on the scenario. The increase in costs from 1 m to 2 m is only 2.0 times the damage cost of the 1 m sea-level rise scenario. The overall difference between the SRES scenarios is small.
Secondly, the authors broke apart the damage costs by socio-economic and sea-level rise scenarios. At a 0.5 m sea-level rise, protection costs followed by wetland loss are the most important damage cost component for each socio-economic situation. Protection costs are affected by dryland loss and migration costs more so than the socio-economic scenario. When the sea-level rise is then increased to 1.0 m, the wetland costs are the damage components that react roughly linearly. Protection costs increase between 4.2 to 6.6 times compared to the lower sea-level rise while dryland loss and migration costs increase by an order of magnitude. While the step from 0.5 m to 1 m sea-level rise changed the distribution of costs between the four components significantly, the step to the 2 m scenario has no such surprises. All costs roughly double compared to the 1 m scenario. This is not surprising since the model does not have a change in cost assumptions in this step.
Thirdly, sea-level rise damages are not evenly distributed over the world. The regional distribution of the costs shows that a few regions experience most of the costs, especially South Asia, South America, North America, Europe, East Asia, and Central America. Next, under a scenario of no protection, the costs of sea-level rise increase greatly due to the increase in land loss and population displacement; this scenario shows the significant benefits of the protection response in reducing the overall costs of sea-level rise. Furthermore, dikes along the coastline can significantly lower total damages, but only when economic growth enables this sometimes costly investment in protection to occur. Hence protection and economic growth are coupled. In densely populated and rich countries, dike building has a high return in that a small expense prevents substantial damage. If people are dispersed and poor, the pay-off to coastal protection is much smaller. For the 0.5 m sea-level rise, total damages are between 3.4 and 3.7 times higher when no protection is built for that scenario. For 1 m and 2 m sea-level rise the damages in the no-protection scenario are only around 1.4 times as high compared to a protection scenario. This change is due to an increase in magnitude of protection costs as illustrated previously.

Protection may not become as widespread as suggested in this analysis, especially for the 2 m sea-level rise scenario. The aggregated scale of analysis in FUND may overestimate the extent of likely protection in certain countries. Also, the SRES socio-economic scenarios are quite optimistic about future economic growth. Lower growth will reduce the capactiy to protect. The benefit-cost approach implies a proactive approach to protection, while historical experience shows that protection is in reaction to actual or near coastal disaster. Lastly, the economics of who pays and who benefits in coastal protection influence society’s choices and ability to protect the coast. Despite all of this, the authors assert that the FUND analysis shows that protection is more likely and rational than is widely assumed, even with a large rise in sea level.

Positional and Morphological Adjustment of Coral Reef Islands Due to Sea Level Rise

Low-lying coral reef islands are considered physically vulnerable to erosion in response to sea-level rise. Webb and Kench (2010) analyzed the physical change in 27 atoll islands located in the central Pacific Ocean over the past 20 to 60 yr, a period over which instrumental records indicate an increase in sea level of the order of 2.0 mm yr− 1. They found that 86% of islands remained stable or increased in area over the timeframe of analysis. Only 14% of study islands exhibited a net reduction in island area. Despite small net changes in area, islands exhibited larger gross changes in island surface configuration and location on the reef platform. Over 65% of islands examined have migrated toward the lagoon. These results contradict widespread perceptions that all reef islands are eroding in response to recent sea level rise. The data illustrate that reef islands are geomorphically resilient landforms that thus far have predominantly remained stable or grown in area over the last 20–60 years. Given this positive trend, reef islands may not disappear from atoll rims and other coral reefs in the near future. However, islands will undergo continued geomorphic change. The pace of geomorphic change may increase with future accelerated sea level rise. The style and magnitude of geomorphic change will likely vary between islands. Therefore, island nations must better understand the pace and diversity of island morphological changes and reconsider the implications for adaptation.¾Michelle Schulte
Webb, A., Kench, P.S., 2010. The dynamic response of reef islands to sea-level rise: Evidence from multi-decadal analysis of island change in the Central Pacific. Global and Planetary Change 72, 234–246.

This study examines the morphological change of 27 atoll islands located in the central Pacific. The islands are located in three Pacific countries, in four atolls, and span 15° of latitude from Mokil atoll in the north (6°41.04′ N) to Funafuti in the South (8°30.59′S). The atolls examined include Funafuti, Tarawa, Pingelap, and Mokil. The atolls vary significantly in terms of size, structure and number of islands distributed on the atoll rim. The atolls also vary in potential exposure to tropical cyclones. All 27 islands in the study are located on atoll reef rims of Holocene age. A total of 27 islands were examined using comparative analysis of historical aerial photography and remotely sensed images. The timeframe of analysis varied from 19 to 61 years depending on aerial photograph coverage and availability. Using ERDAS Imagine 8.4 software and Quickbird satellite imagery, the images were all rectified and ground control points for each island were established. The analysis involved the overlay of the historical time series for each island. Webb and Kench analyzed the islands for areas of accretion or erosion as well as the configuration and position of the island on reef platforms. Changes in island area were calculated and compared to establish change through time.
Webb and Kench show that all islands have undergone physical change over the respective timeframes of analysis and over the period in which the instrumental records indicate an increase in sea level. The data indicate that islands have undergone contrasting morphological adjustments over the period of analysis. Furthermore, the magnitude and styles of island change show considerable variation both within and between atolls in the study. Only 43% of islands have increased in area by more than 3% while 15% of islands underwent net reduction. The net changes in island area mask larger gross changes in surface configuration and location on the reef platform. Modes of island change include: ocean shoreline displacement toward the lagoon, lagoon shoreline progradation, and, extension of the ends of elongate islands. Over 50% of the examined islands experienced ocean shoreline adjustment via erosion. Additionally, accretion of lagoon shorelines was detected in 70% of the islands. Collectively these adjustments represent net lagoon-ward migration of islands in 65% of cases. The results show that a significant number of islands exhibit ocean shoreline erosion which may reflect shore readjustment to increased sea levels over the study period and potentially increased wave energy incident at shorelines.
Webb and Kench illustrate that reef islands are morphologically dynamic features that can change their position on reef platforms at a range of timescales. The mechanisms that drive island change can include a combination of sea-level rise, decadal-scale variations in wind and wave climate and anthropogenic impacts. This study contradicts existing paradigms of island response and has significant implications for the consideration of island stability under ongoing sea-level rise in the central Pacific. First, islands are geomorphologically persistent features on atoll reef platforms and can increase in island area despite sea-level change. Second, islands are dynamic landforms that undergo a range of physical adjustments in responses to changing boundary conditions, of which sea level is just one factor. Third, erosion of island shorelines must be reconsidered in the context of physical adjustments of the entire island shoreline as an island may experience both erosion and accretion at opposite points. The authors conclude that the style and magnitude of geomorphic change will likely vary between islands. Therefore, island nations must place a high priority on resolving the precise styles and rates of change that will occur over the next century and reconsider the implications for adaptation.

Sea Level Rise Expected to Flood Mangrove Forests in Bangladesh, Killing Native Tiger Populations by 2100

The Sundarbans mangrove ecosystem is recognized as a global priority for biodiversity conservation, housing the only tiger (Panthera tigris) population in the world adapted for life in mangrove forests. The mean elevation of most of the Sundarbans is less than one meter above sea level. Consequently, sea level rise (SLR) poses the single greatest climate change threat to the viability of the Sundarbans forests. Using scale-appropriate elevation data, Loucks et al. (2010) illustrate that the Sundarbans, and its biodiversity, is vulnerable to small increases in sea level.  Both tiger habitat and tiger populations will likely reach a critical threshold at SLR between 24 and 28 cm. At a 28 cm rise in sea level, the Sundarbans tiger population is unlikely to remain viable. The authors assert that a 28 cm sea level rise is likely to occur around 2070. If actions to both limit green house gas emissions and to increase resilience of the Sundarbans are not initiated soon, these tigers will become early victims of climate change-induced habitat loss.¾Michelle Schulte

Loucks, C., Barber-Meyer, S., Hossain, M.A.A., Barlow, A., Chowdhury, R.M., 2010. Sea level rise and tigers: Predicted impacts to Bangladesh’s Sundarbans mangroves. Climatic Change 98, 291­­–298.

Given that only a small portion of the land surface of the Sundarbans is over 1 m above sea level (asl), Loucks et al. estimated sea level rise in mm asl. They used 80,584 elevation points to create a continuous digital elevation model (DEM) with sub-meter accuracy.  The DEM was based on 1991 elevation data collected by FINNMAP. The authors used 4 mm year -1 as a conservative estimate of annual SLR upon which to predict potential impacts to tiger habitat. So as to account for the difference in sea level from 1991 to 2000, the authors factored in a 3.6 cm increase in sea level, equivalent to a SLR of 4 mm yr-1 for nine years. Next, for each of the time steps, the authors identified the land area that would fall below the rising elevation of the sea. This land area would be permanently underwater, and thus removed from the potential habitat layer. This analysis was repeated for each time step. Increasing sea level can cause both direct habitat loss as well as increased fragmentation resulting from new or expanded streams and channels.  To estimate tiger populations at each time step, the authors utilized a maximum dispersal distance of 5 km across water between potential tiger habitat patches. Second, they defined the minimum potential tiger habitat patch as being 10 km2.  To assess the potential range of tiger population for each time step, the authors factored in the relative tiger abundance of each patch of land, the average female tiger’s home range size, as well as the female:male ratio.
Both tiger habitat and tiger populations will likely reach a critical threshold at SLR at 24– 28 cm. At a 28 cm rise in sea level, the Sundarbans tiger population is unlikely to remain viable as the remaining habitat will have decreased to 3.8%, and the number of breeding individuals will be less than 20. Using a conservative rate of a 4 cm per decade increase, which is consistent with the 4th IPCC report on sea level rise and local tidal gauge records, the authors predict the Sundarbans will realize a 28 cm increase in sea level between 2060 and 2100. Uncertainty persists within the study because the authors only assessed tiger abundance in relation to habitat size. The biodiversity of the ecosystem relies on intricate and complex abiotic and biotic interactions. There is uncertainty regarding prey abundance and possible prey responses to SLR that could then alter tiger abundance. Furthermore, the study does not incorporate potential effects of geological processes, drainage, withdrawal of water, and sedimentation; factors which may reduce or increase the level of permanent inundation. In addition, there is likely a time lag from inundation to non-use of the area by tigers or their prey that was not accounted for.
Although there is considerable uncertainty regarding the degree of future habitat loss due to SLR, it is still imperative to act now to mitigate the potential habitat loss. Globally, action should include limits on carbon emissions to slow climate change. Locally, management activities that conserve habitat or limit threats include building dykes, developing and planting mangroves that can adapt to the rising seas and changing salinity, and limiting poaching or killing of tigers and their prey.